Arcana Analytics

Jul 21, 2026

Senior AI Engineer — Inference & Agent Systems

United States

PythonPostgreSQLDockerGo

What We're Building

Title: Applied AI Engineer — Inference & Agent Systems

Arcana is building AI agents that synthesize information across heterogeneous sources and deliver structured, reasoned answers in real time. The product only works if the agents are fast, reliable, and correct, not approximately correct.

Our stack: Go + Temporal for orchestration, a Plan-Execute-Synthesize agent architecture, and an evaluation harness we use to measure every regression. The problems are hard. The latency bar is aggressive. The accuracy requirements are unforgiving.

Why This Role

The problems here don't have blog posts about them yet. Parallel agent DAG execution under hard latency budgets, streaming synthesis across partial sub-agent results, eval harnesses for non-deterministic multi-step systems: these are genuinely unsolved at production quality. Small team. High ownership. Every engineer's decisions ship to production.

Apply

Location: United States

Send to: [careers@arcana.io]

Include:

One system you built where latency was the primary constraint what you measured, what you changed, what moved

Link to anything public (code, writing, talks)

No cover letter required

We respond to every application.

The Work

Inference Optimization

Drive TTFT below 400ms for multi-step agent pipelines

Streaming optimization: first token to user while sub-agents are still running

KV cache strategy, prompt compression, dynamic context window management

Multi-provider routing: model selection by latency, cost, and task type across OpenAI, Anthropic, Gemini, and open-weight models

Agent Architecture

Design and implement Plan-Execute-Synthesize pipelines that run sub-agents in parallel DAGs, not sequential chains

Build reliable orchestration on top of Temporal: retries, timeouts, partial failure recovery, idempotency

Structured output enforcement: JSON schema validation, retry loops on malformed LLM output, graceful degradation

Tool call design: schema design that LLMs actually follow reliably across providers

Evaluation & Harness

Own the eval framework end to end: ground truth datasets, automated scoring pipelines, regression detection on every PR

LLM-as-judge pipelines for qualitative output assessment

Latency regression testing - p50/p95/p99 tracked across every deployment

Adversarial test case design: ambiguous queries, missing data, conflicting sources, malformed tool responses

Infrastructure

Model serving and cold start optimization

Async worker architecture for parallel sub-agent execution

Observability: trace every token, every tool call, every synthesis step

What We're Looking For

You've built something that runs in production at a meaningful scale and you understand why it's fast (or why it isn't).

Strong signal

You've worked on inference pipelines where TTFT was the primary metric and you moved it meaningfully

You've built multi-step agent systems and you know where they break not from reading papers but from watching them fail in production

You've written eval harnesses from scratch and you have opinions about what makes a ground truth dataset actually useful -

You've debugged LLM non-determinism in production and built systems resilient to it

You've worked with streaming LLM responses and built infrastructure around partial output handling

Stack familiarity (we care more about depth than match): Go, Python, Temporal, Kafka, PostgreSQL, Docker

Who We Want to Hear From

You've shipped inference systems at:

A real-time AI product (search, coding assistant, chat at scale)

A model serving infrastructure company

An agent platform (any domain)

Or you've built eval/harness infrastructure that a team of 10+ engineers actually trusted to catch regressions.

Weaker signal (but not disqualifying)

You've fine-tuned models but haven't shipped inference systems

You've used LangChain/LlamaIndex but haven't built the layer underneath

Strong ML research background without systems exposure

Languages